{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 首先 import 必要的模块\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.metrics import log_loss  \n",
    "#SVM并不能直接输出各类的概率，所以在这个例子中我们用正确率作为模型预测性能的度量\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据 & 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"diabetes.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 各属性的统计特性\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MACxYsKBMWIZhGIYjhb8JiMhlwAXAJZpwTUlVd/j/7wK+ALwuqTxVHVXVYVUdnjdvXtGw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\nGEYPMxMHgdFOB5BCN8cGFl9Zujm+bo4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad302bddd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data[\"Pregnancies\"].values,color='purple')\n",
    "plt.title(\"Distribution of Pregnancies\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oF1wewOTYJF56/iXtOWyeCDZclneqDmg7j4xpgktrTkp+rra0hukXpluyYMoT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\nBH8gEAj0GEHwBwKBQI8RBH8gEAj0GP8/6NVEWnJhgaIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad302d0b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data[\"Glucose\"].values,color='purple')\n",
    "plt.title(\"Distribution of Glucose\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PM3NbnPOLOC0uikwuU93xLePYuGAjNvRswMYFGzvq8lb1KXpZyp9VPy/K+1IVuiJWTxGn\nxUWRyXaqW5QZSkDVp+hlKX9W/bwo70tV6ArFb5oWd8rOXqSpus1U1zTi6pSy6YYpepr+V4byZ9XP\ndelQD2F8y3jh6sHUrqpzf5deu7RQZeiKWD26afHCZQs7tmuvLFP1ABlxZU8Vdo1m1c913lQ8xYWr\nM1O7Buf+Bkof8HZ/3/3+uwtVhq5Q/LrAV7vu3dUxu2HZgnHpRmhFW0wsE1WwW2fVz4N0qHfmVqCi\n1ZmpXXXn/k4dnipUGbrC1KNDO4rdM4ENtKF5YEOcq17S6bpqql4EF08VS65e0mLjB4o9QykDZZxF\nJemfWZmkhlcPY/TiUeV3RaqzpO06sXeiMO9/Vyh+3cJkfU69ZcoVJfydbjEzy0XPoi2ghinLYmKZ\nKNI6jw1F6J9lqLM4GXU7v+tz6h2v34CuMPXopl4AUgfgynK6XvSp//DqYazbvQ7rp9dj3e51ovRT\nUrZ1niL0zzLUmUlG3bm/vf29ANDx+g3oihG/borV2N9AfbA+o7KNae2ZwIaeDc0Rr8lcZPI2UE3p\nspz6h9PPY3epbkoavb5w2ULsundXR2YJRZk26+TIchYV50WibJM9E6BeAk8xBuYPNJWnLh3dSHVi\nzwSuOfkaa8+UtJ5MJhnzwkVmGxmjXj1nXXAWxq5Xb2vqhBkr9iCWdpD2IBZtYC2CVbRFHbVZNfTV\n+7TmotqsmnIhKzpljktrYP4A1u1eZy2XKn0budKkX5tVw6I1i7Bz807jD2navNPK2O4F9HbIYcoD\ngLEvhOmp9YCIMHV4SpnO6MWjxvelt78X5918nrFcRWkXF/KWOe59dX3/w+R5EEvhUbqCpVT6QLy5\nSDdNczE9JZnGqtK3kStN+pOHJrFj045YBdOuqWsRzBLtkiPOi8R2Rjs9Od2i9KPpxL0vNp4pRWkX\nF/KW2dRGnTJjdYWpRzX1MoXWDaa/Ya8eXadv7G/g9CWn47Htjym/V03TtKanAw2sunXVjCki4M9a\nLKaZ41vGY8MGm2SwQfcsT9n9ktrkndZUZWM2a4dZyug5FjIZRh0GXEwZpjyywKWvxJk4s2oXoH3m\nHlP9blywMd7bL8akZvpB7dRMqCsUP9DqUja+ZVw7bdVNq0xx2HVKH1B7G5hW/aOuby6eFMG9NqTx\ngtDuovQ7d9q8o2W28a6ylTHIW1WvYRtrVh4VxkEGz8wniedM3EAmLXHeKFFM8mbRLnddcleLSSpv\n7xdT/dp6+wXvhUp+HfXBesfMX11h6ominbYStNMq1yP4AP00zcUzwWWaaTutTzt91Mm/eO3i2Dqy\nyTsLU1VcHdvUVRbTeZt+E84niVkhSd+0xn8nXPIwyZtFu5hMUnmw5Ool6iOkDHmbyqGSv2h0peLX\nTl1ZP2IIdg66oJumuexmdPH0sZ2Sp50+6uRfft3yGddHLh1x3rVpU464e+Lq2Lau0npUROWIyyeJ\nZ1dLHiaibQIod8K2wEdny+Fy1AfrqB2n/yHQyZtVu7jkmZbh1cOx6xvRvLOQpXFAv8cob7rG1BNG\nO92MeXGGVw83bXZxxL1QtrsZXTasxG1IA7wyZrWLMjq1Da9DrLp1ldKdMBgZmXYsU0+8ycjGVBXI\nGKQ9evEotl+1HUuuXmJtHqnPqVuvr8TJAehNhkF5km5QCvK45uRrEnmG6eQK1rmi5Yh7LipvnPvv\n6MWjGBgasOrDOtIGXTQ9OzDf3F/Cpqptl29L7TgSTrMTdOWIP80mENspr2vwKF2se1tZx7eM46Xn\nXzLmkcbEY4rFbwpKtfWyrRi9eNQYiCz6fJzS7+3vtS6HTrY5r5wT+2xPrQeHXzicaRA1U3uObxnH\n4RcPz3jGtt10fcCmvnQbiw6/cHhGecN94fCLh5ubj3Ty6tpg62VbZ1x/6fmXZqRnQ9qgi3EB80zv\nfbj9ogHYkuLSx/OgKxV/msBRqmdPX3K68l5bu6Op09nKqgv+RD2UOghc3Euhs0tvu3wbxm4YmzH6\nidaLi8shAPQf329dDp1su7+1W3k/9R6tr2NOOCZzW7KuPQFvUTSqNOqDdet20/UBm/oaXj2MY044\nZsb1qItmtC809jfA7HvAafqZi/vv9OQ0+o/vV5sLNVAvpQ66GLe2EjWnBTP6cHl19W+SW2U2qw/W\nY/dD5E1XmnqiblYTe9QmCNOOy/B9Gxds1OZlY+uLi3UfdUdVyap1sWTG+un1sTIEqMq87fJtRvlM\nO6N1hKfNrvbQxv6G1hVyRj6Orqdhl7s0AcFULokP3f5Qy27NsDls44KNyh+//tn9Vn0S0Hvd2NqK\ndfeFy6vqq9OT0+if3Y+PPvPR2OfD6Nqgsb+hTGtDzwalCYWn2RzALeQ6q3PZtVlbiTPPuvZjnmKA\nvDaWePw50zJiQaubVXgU6xIr3dTgNna6uE5nI0sWYZNV+ZimroF8iWyRhKb8iZ63nMonSTscxC9J\nmqp6HLt+zBiD3da/3RTnXbd4bFsHNn0oyeKzLl3tOliob7jIZyxnqB1U9ZfF+5NnP243Xaf4TWaF\npG512gY3uIfaPB9ct5Eli+BVutFcnNyJ3AkZTflVz6vszSrycnVMs5Pa1nQVNqPYKJ64HbquLspR\nbPpQEgVpcv9V/liF+oaLfEnaOqi/LN4f3TqJixxFoetMPbExsf3deNoNG4qY2QuXLZwZo4aAkQ+O\nGL1XgqlmXKx7292fi9Yswo5NO8BTDOolLFqzSOlZo9sJ6zpVXbhsIYCjJqfRi9RTbR1BfrqgVrbp\nmXaLRtN28bYIdlKHA2pNHprE6EWjGL1otBlcK2o6SLLT1WZRN9EO3ZAStemLKzatMHrFqPoqyLyL\n1RS0TBuYzK+X6HPz3jgPu7+1e0YfD8oyeWjSeiNhOK9tl29r+RGqHVdD37F9GL1oFHeuubPFBBgX\nnC3cX1wo0pkCXRGkLYxJqbegieVTH6zjSOOIMkBZ3Hb/uGBauhfORua4AFs2QdtsXVWjz7nIGSap\ni6GNLFmkGZyFahvkLJDDFLhPRW9/74xFZNU5rGmCDUbrJ03gsfAaWTRv1+BlpvZQ9WlVuVTBAZO0\ngw1p+1mwOKz7LmkwNh1Jg7R1neKPi4TXgqJTp4mgaeoMpmedZFakDcRvtw9GM675hGUP3Nmi5iHq\nJfT09ih/lFwjOdrKkkWa9cE6+mf3O4dDUA0OXFGVRSm7Q7DBcJpJ+2KYLNJI07cDdCP8LNpBRdJ+\nZhqI5RWhNKniT2XqIaIPA/h9eF1zHMAlAE4BcBuAOQAeAHAxM8+c5+aAcjpoenHY37gRNo/ELHSa\nMJmPTPIG+ffV+4wB41zyVN03evEo6nMczyfwTV/3/ME9mDw487lg5Lr3vr0zzFBAfPC5vnpfU57Y\nuOV+XPiw50x41Kxqf9PGnMaBRqLdk6pgewuXLdTKrSxLaGE/nE50ZunyoxQ2iZkWaW1j++v6oS4N\nQD2r3XvfXqe6iWLyDqoPJntvTMTVEaA3bwXXon2waKfZJR7xE9GpAL4L4ExmbhDR7QDuBbAMwCgz\n30ZENwDYyczXm9LKYsSv+gWOm0oGv+w2oxKbUbtLYDhTzHuVb3ynqA/W0Xi2ASjWgEcuHcHy65Zb\n13141GMaMbmYpIIY8YB6lDXvjfO0QfZsZ0uq51wD/anS0K39pDGvxdWhzpTpEttflYauvW3OcEhL\n1u9N7bgawHAesXfiLIJOxePvA1Anoj4AswA8AeCtAO7wv98MYGXKPKzQeazolL5L4ChbTw8XrwvT\nppeiKP2mF4TG8WfHph0A7Ove1qtKe76CgsBzRpeeKbKqa3AywNwXdN5Lup2vSb25TMR5sQT3qJ6x\n8VbSpaFrb9MZDqq6SULW743KfGTjlVOmswgSK35m/jmAzwHYC0/hTwDYAeA5Zj7i37YPwKmq54lo\nLRGNEdHY008/nVSMJq4r5raBo1IHHtMEhksb8z4vojtOTaaQ5h4JFy8Xi2Blqt2vppd6Yu9EIo+J\nGcHJgJYfmPpg3SkInUrulbesxHk3n6dMw3ZTkWvwQF0dmtoztg4t+0QUU38O6iYLsnxveFqdVqzH\nYII9EJ0ijannJAB/D+DdAJ4D8Hf+3+uZ+ZX+PacBuJeZjVozC1OPy5SYegk8zU27nG5arJvSqw5g\n0C1AuZoFbFzVqIe0nbM+WMcvn/2l9nsTKll1QcECWT955JPO5gggvgxAqzuqyfyTZpHW5fAX18Nj\ntl62tWXdY/HaxVh+3fLm96aAa80y+3m5tGnL85FDQg6/eFibJ6A2e1Ev4fzN5yc2P6lotrHBK8fU\nR1Qy2ij/+mB8kDhTvuFDnI788kjLupfuuaiOyNLm3wlTz9sAPMbMTzPzJIBRAL8J4ETf9AMA8wA8\nniIPa2yn2YA/OgjtqFu4bKH15g7dzmBdpwt84W3kDTa9xG0S0XXKnloPfvlcvIIwmR/CxAWGW7x2\nMQC3uo8rA+Apg8b+xow20tXL4RcOK9swjmgepp2Vqhg2pue3XrYVY9ePtfSPsevHsPWyrc30dAHX\nosHIGvsb6vrSdJM5r5yj3b2uCpIWtL3OrBQNSJikvaM0DjSMCrin1mMMcx2V3+asCACxQeLi8g3q\nsrG/McPZQdenozqiCDt50yj+vQDeQESziIgALAHwYwDfBPBO/541AO5OJ6IdNtNs1RbyyUOT2HXv\nLuugbq4Bx3bdu8ta3iDmvSqYVhxB0DHdD1A4mJvJ/BDGFJQqWNjVlaUlDwOx8eJxtI1W3rLSK0eE\nqcNTzTa0SU+Xh8kW63p4TLD+ESW8LqILuKYKRhaFegn1k9QhJ3Z/a7fxkJBokLSg7YN21L0nyoBm\nkfa2rn/D2CSuLwOtwfZUZ0XozhKIlr8+WG8JQBeXrw2BbKa66LTtP7E7JzPfT0R3wHPZPALgBwA2\nAdgK4DYi+ox/7aYsBLXBFGTJZCqY2DNhHT/f1V6nO2u0NquGI40jzVFCOJCcs4shAet2r/OCXGng\naW4ZyZhi2dusfey6dxc20Abl9DXqCmfC+hzfPRPYe99e6EyTgaxpXlpTeW1MG+HnTUHiTHnZupjy\nFGtHzHF10DjQGiQtetaC7vnwrned2UIXSM0a3xkibld3YG4J5x3t0xMH9cEF+2f3A/ACqIXT2ED6\nd8gWnvYCJ5reR8C8GzpvUvnxM/N6ANHQkI8CeF2adLPEdgPJ1su2tthfdbgeJKE7a1TlEx8OHpYk\nj1i/78gZsECr+170fFGTHLogeHvv29vivqfa/ZmUsevH0D+7Xxn+AADuXHOn9lkbG7DuoA8vAcSW\nIfwjZ8rv032f1qblev6tiriyhuVUnYFrKquu3QGvz6Q9H7g+p259rnQQCC/IG7B83+loOaLvQxZ9\nlXqoGRguri6i9dcuui5IWxRb04xuah7G5jCUMK5njQL64GE2edgGkTK576WZgmrd9zJ0VDp88LC6\nbkwHvBDibcAE7UEfNicuRddIgvUPFTo542ztNsTZu6NyKvulY3uF+0yaw0UC27uLKTV6nkDse6ZQ\n7LGB8BwJ1kRs1506YfYpfcgGpafF/oZzICcAWM8z49q7HBkYBH5SeXroYo3rcPHOCbwJBubPjAuv\nf8j/XzfyjDmKLhGWo6lYz4sEo7JVX1wFwBxozsbjQymOxmsjvJvZNg1VGzb7VUyd1Oe0epoE/cLk\nTZKFaSPIPzgXIkmaVmU0ZW/hAZRLnzZB3g+tanavutflXI3mYx3awNVRtJ4WcLf1qhZiXI8MnDw4\niSONI1h16yqs272u5QVzjeWt9eRQEF4n2Ll5J5ZeuxQjl5r7wsDQgDHcdNIXxLi4x/HhmGuzalh6\n7dLYdFwJzFAmjw1XpVMfrKM2q6b12hg6Zwjnbz7fKq3ws9HY/mBg6bVLW87Hjcqx6tZVeOn5l1qU\nDE8zevt7cf7m87Ge18/ok6YY/65EzVwuUC9henI61rXThI3SX7d7XfyB9VnCapOuinafv1tqxe/q\nYWNCNTVPkr5u2rbk6iWZvWQ2+ZtMV0aTQsxo2uS2Z+NWF3d0XSC/yVSShKx3d+p2sIbza5oPUmK7\na1RVt1FTSPSZTOojsjvdte14imMjdPbVky9HRs2hWb6HtVk17Q+yNQ5nKmRFaePxj28Zz2za1j+7\nH0PnDM24njT9iT0TM6a7QfyPdhAndzheTvgliJsuq8wQYTNTYEYYOmfIOQR0VH6dG2wastzd2YwN\nYyDLHZumtMIzXeWzgTeO5XEcjfRjAAAgAElEQVSEzrBnQtt2+TYsvXZp00kivHltwVsWGMNn6Gge\nk+l4FgQAz0UzYnIdXj2cLC0NLjGOtHTA2l5KxR+YYFxo7u701wIOv3C4Oco4/OLhGSvrTaWYUaPY\nTvnyhnqpqfSjIZbjlL4qqFjfsX0z9gAEbnVpdni2POfaDhm2m46dm3ei/zi9hxHg2dyT7CjOIy2V\nF4uLB47N7uiwl83y65Y3fwCSvK9BnkEQRdc2NQVVdLH1GwP5+YOmLCKQttuzp5SmHmcTDHk20nW7\n12H99Hr0z+43BhAL8ihKsLQsCabhps1Z0amwS1CxMFns8ATgtYPL9NzUbhlN8ycPTeLwwfho42mO\n61OlldTbJ4wxGF7K/FWmpbQmWdd3MS6oom2bhE2icUdIpp2httuzp5SK33mKGgmUZhNMqV2BlcK7\nBpsBwUwkVFzUSy27bY3l45nB2myDioWx2U1tDR8dfSXdnduSThY/ADHKqHGggeHVw1h5y8oWO3AS\nm3CQVrg+06ANhmebf0zapr/DmOoi2Mjm8i7aBFXUtYkuIN/w6mHj2QSxMlq2VTuDuZXSnTOpCSEa\nvCrJPUncRG3liUs7CC4X51ZqzMe38+qCdYXzmXHAhq7OfVfCsBtr85nIRqjweaUuQbjCprqBoQE0\n9jeMZhYd4TMYgrNW80J3DkOS9Q/qITBz89CXXffuymyNK3ygTZrggSpZbYIgGvN06O/afhsTVM8m\n+F7caWRpjs0MqA/WW3ZU21CpoxfTHOdmcziE6ZzPrA6WaPchFTPy6YE2zn4Ym3NRbZ7pqfWApzmR\nolUd7ZgEmwNHkpTXlFfUfTLtMYR5YTrQJg2md8l0KE9a4trR9bxq1X1Zl6Gnrwcrv7DSyc5fKT/+\n6BTVZeo/IyibzT2GYFC2edeOq7WYdVZsWmEVjMtEXDAo6iV9PhZKHzAfZuPyzPTkdPLRNSO1Ig7q\nYnj1sNbmTL3kHmws8jxgDnrXCaVfO64WW57ANp/m3VJhEwTRNk8XWeL6re3hM8F9uqCKrmUwynxk\num12/lKO+KO47ooF4eiZqabpssIdzCVv1U5g22et8Hf7aXdKEjDywZFU3gbdRNAe2npPufs0SCOc\ndtiMktku2byI7B4d3zKejeuj465U43uRpcdWzO718H3RejGdxwukeLcd66pSI/4orrvegkBQsTZS\nTaz1aFo64uJt28qt27UYBBXTLR7VZtVKpfTTji7NiR9tD129hwPqJV48jbzsgYvj1su2tmUDXxpU\nwduywPSOxMnRArmnFZePTXqqeonGc4q+66Z329TP27WDtysUv4tbWtyOSxVJXa3inrGVu6/epz0o\nxnTW75HGEcUXxcTlMI1EhFzvdIfghBcEs3TlnTo8VaizlFX09vfGB29rEyb3SWBmAEOVi7BNaBCb\n3bI2Qe1U+kHnMtrb36s9bCnaBnlSasU/vmUc15x8DUYvGsXkocnmyHiGa2SoE/XVkwWCCnbjbqAN\nuObka7D1sq3YuGCjecdkjHuWrXvc5MFJ9NX7ZqwRDK8e1s9aOD5+SScIu9CF20u1fpI2/ShBe8TZ\na/Nwq0vrPRQ+WER3yEhSqMdbBL1zzZ3YQBsyOVoxTGN/A5/u+3Tz9LE4TO6TjQMNq3ONV96y0phH\n0N6msw9UazW2Ls06l9Hzbj4Py69brv2uXRu4SmvjV+08BY56Jxjjc7dhZydg3j2oIu6Fi3qKmGyw\n9cG6dwyjq8LJsW5c6+PTfZ+2kj9aL3Gud3E4KT7L+jK6Q8akUR+s40jjSOZ9WJluhumrCO8lMZG2\nDW3TcM1Hd05yElfMLKicjd82KJU23njO9tZg2hacbrShxxtJmez+caafyUOTGL1otJlOnCnJNVhW\nkh2mPX12z9hOrcPYyh+daseZcuJw2tFq0ZeC6b02TUMaWtNkyj4ca/LM6R2xOfcC0LQB6c+wtk0j\n2g/S9pWyUspYPUDMMXk2O3BzHPEHnhyA/oQrQL3Bqa/eF2tbDdIx3dfY38BDtz9kLzTFR86M3h/E\nqglGs/XBOl564SVMH25Nh3qpqZz33rfX23xk8IgIWH7dcuvFadUxdjrPC90ZDmF5F7xlAQ48csBu\n5M+hEb1ipDx1eApj14+hPlg3K9og/8j5Cto6YP0ZAtRLTXNfS3lC9RHrscOhuDYZzQCcZqDRHx4G\nHrjxAQydM2RlEtH1AwAzjpDUnVmw9bKtLcHmdPI7H5faYUqr+E3HAoZXxrWBqHKaykankaqFoG2X\nb2uZYk/smcBdl9zltHHIZuGtpX7iymtZF+FgbcHOWZ5i1GbVcNYFZ2Hn5p0zFH801nyAzbFzLgG1\noump0oya/lR9iKcYj21/DCOXjljvkA3qYNGaRRj76zHlPonG/oZVv+vp62nZTKXFdGTBNDfz4SnG\nvn/ZN8NMGEd4l7PL5qSB+QN4ft/zSiVp47llym96chrbLt9mbQuP9oNo2uGzFIKRfljph/ur7XGW\nZaC0ph4TcVO5PO3YNrONxv7GjE6dZKOUExlM3eOCtSmPXYwhzmPKNZBYXHou3io7Nu1wit/ejPlv\nmjhZ9LvAXBkrKxsOj4nkowxCaCDJsaHh53RmOhvzXVx+SU/piks7Wke2ZqkymoZKO+I3Ta2iIYKB\n1ulensevhQ/sLpz7Xgp5wlPg0YvVJoKknisTeyYwvmW8JY5PeOMT0Np+cbFqoj+40bRt4Sn2yupQ\nrKxi/+TRRwPPtLgjJsO7nAGLAGR+kcNtFjwbNpMsXru4ZWE32i4uxy+ObxlP5AET57EV/t44wp8/\nYGWuVPXrsy44y9rcmRelVfw6Ba5yjYxO9+K8NpIGYqvNqjUP7C5iPJYkRL2kALc47rZEA6aFY7vr\nzDZaj4zIhhuV95c1Ofx4ZxnoT50BjHLHnd17/ubzW+rbZFYN5xPdNxKOyR9F1S6TByetz61IGr8+\nru9Gj5DUtZONslaVsbG/4WzuzIPSmnrSrMabzAfBRiKb6X1PrSez+DvKOPUGGU5fcnp+m51CqOKr\na81nKVC9YKZjA3VyqDbcJFb6OeDSv5JAvYSRD47Y9Y2oDH6Ij6QKyGWjY9p2SbqpMu7dtz1C0iZv\n2zK2OxY/UGLFH7cJx/pZzAyutfy65bGHeQSbRD76zEexfvroQdZJNv/oNqGYZHjv1987o/zheOL1\nwXrzRyktqs0pM+K4t2HNJIpNH4hrj2Y92UJ2G6hU91j3Lw02bcrT3LoRzkTk3IVVt65SjtBdPFZs\n+38Wm+SSpBH37of7jmm/gU3eLvK1MxY/kNLUQ0QnArgRwKvhdeX3A/gJgC8DWABgN4ALmPnZVFJq\nUJkA4gIoRb9f9cVVyh8LnTdJ3AYSVzMI9dIMGbddvs08HfU7rcpjITgJqH92v1PcduOGL/Y2roRt\n7q7ms6SovCV0bRxcH714tHnd1B66ePm6OP0tsdcPmss66+RZWPLXenOAi7eSStY4M1fQProNRwAA\n8o4dBQPP73u+eW4u0BqP3qlP+30lmka0Hozmo3B55uvPjgjW01RrQ4DajVP37qv6zvDqYW07UQ+1\nrDOo5LAtY1Af7STVzl0i2gzgO8x8IxH1A5gF4OMADjDzZ4noCgAnMfPHTOmkjc4ZoHIDi4uZrYqb\nbpOWSQZXm3I45nfcsy7yutDT14PpI2aZVfb+LPIPRl1RZavKT9cupvMTHrjxgdgd3jZlCHad2kZe\nNPUXl/qyje2v2tmdan0D2ZwbkVSuuPY7+wNnK79Tnd+gis9vE2tf9Z3qHlV5dP1ahendMtH2nbtE\ndAKANwO4CQCY+TAzPwfgPACb/ds2AzAHzciQuABKLmfGJjUlDa8exjEnHKP+UjNFD8cGN70MJhnS\nBtWKU/qA2eauqq+o6UkXZfTYE4/F+ZvPt4pd4uJKGsSCt42LEleHwWzK1mfbZLs11hfiY/vb9M8s\n1jdU8fRdo6iqXEl1cqnWzFT39h/fr/2Op1h5HoTunG2TXgjqWVXmuPeWpxjHnnjsjL7XP7t/xr1x\n61lZk8bUcwaApwHcQkSLAOwAcDmAlzPzEwDAzE8Q0ctUDxPRWgBrAWBoaCiFGEeJC6Bk6/6XFq1N\n1PDDHzuVJhhNTO2yEcbZ3E0/jBt61PHog/NcVSa56NRbl79uVBWcLWvjgRHXBsHuYJedrGnqy4RN\nTPis+kS0DnXtGJeGjVz9s/tbTIo61+HGgUYmu2Wbban6zsK1M66OG/sbM0xKuh3T7bTzp1nc7QPw\nWgDXM/PZAA4CuML2YWbexMwjzDwyd+7cFGIcxRRn3RRjXWdHtom77SJHGuLSbNfOwTT5xMXBDzDV\nvWv+Nvdbx56n0A90aEPcwPwB7QJxHu2SRUx4F6LpJEk3upteR7Qspj6TSfkMMf4DvWHqGzZyBOWx\nSatdpFH8+wDsY+b7/b/vgPdD8CQRnQIA/v9PpRPRHpN7nyluvcoF1MUsZCNHGmzidFvlaZihz3Al\nTSiHCVsXXFPdO7mSato2ipWZTDXC56OLrkuvXdq2YF8uMeHT9kNVGVzTtQmMFiZcFlOf0cW8d0IT\n49+0S91VDptjHtu9+zdxrTHzLwD8jIhe5V9aAuDHAL4CYI1/bQ2Au1NJ6IDJ7hkXrC0aQdM27nas\nHCYsTKUqe7Qq4mecjT3O5TLqSjpy6UiieOGmaKS69gFa699kknNyJWW7TTFxbWrKI5A1jXuxDl1d\nusSEV/WJOKLnJBjXF+LSCgXoC+S3ed7m7ARVzPskqGL8x+oNoKVutOt6ofLYptUO0nr1vAaeO2c/\ngEcBXALvx+R2AEMA9gJ4FzMfMKWTlVePCZ1CUcUkr82qaQ9scYkHbrLnUS/hk0c+qVd05J0LnJW3\nEZBNjHMTSWRzOS9BJ2de8fdt4rbr2iktprrUnRWd5XkDafuU7r2KpplVn0zjTmyKpR8nn613VvAj\nl/X715F4/Mz8Q99O/+vMvJKZn2Xm/cy8hJkX+v8blX670E0ZgZmRLoO/XabuqtGZySwU7Ao0HTOn\nej6NCWrhsoUz8spyiplENtvzEkxy5hF/X2WecGmntLiau7I+byCNWdP0XtmYo5L0yazNq6Z0XQPY\nhU1CRYn9X9pYPa7oYnObvAZW3boq1nMCmDk6s4mXP3bDGHbdu8trdJ0ZQTE1TGqCGt8yjp2bd7bm\nRcCiNYsSjVZVXiVJZDOZ4GwDYena1sYsFY7L31fva9l0BPgjPj9Nl3ZKQjj2u47A3AW4lTfaXovW\nLGoGCktariDNyUOTM+LZ694rVZrhMyiiwfniaJYr4p3TEgwtFHdfhU3AR11dxwWwU7WNaz/Ng8oo\nfkDtPqedNg8NWLvb6UZnxmBcIW8MXbRE1Sq/NjhdjEeAbmQd+Ka7oPuh0+1UNMlmCrbnMv11dY1U\nxeWvzao1zTaqMmpNUBl4Y0Rjv+uI7sy1QVWWnZt3Ns0uNsHu4tKMxrM3vVe6NICZgd5cyhUNGDd0\nzlBL2IUk5QTMde3af9O48GZJaWP1ZEUW0y+Tb7nNlDrI00aGpPKmWayOovuhC2Rxka1T098km/1c\nTVAu2MR+T5pXXFmTtEEWaaYxW+qeN6WTR18rkvnGhcor/iy8MbS+xn5aQdo6TF4FWclr60Nvg/aA\nGYdyBOThDWND7Ga/GBNU1rIat/WnzCuurEnaIIs00w5GXGLr28rkSqf6b1oqZerRkXb6FRxFGPVg\nCKa9QdqmqaZKBpUdHUhmIzTJaEIlg256Sz3U3Gkb9nSJ22XaiemvqQwbejZ4594aArVlTZ4x+m3M\ng65t4JqmSwA928GIS2z9sAxZ2tfzSLMdVH7EnwW2v/ou00LV7sy7LrkLd7//7kS7iZOMTHQ7RBcu\nW6g0YfEUz5ArzQ7oPNF5gQRlUCnhPKfwxiMJU9Zbp00cLv0oS++khcsWxsqQph8WtW/bkMqPPyva\n4cdfFGxHCC5+yXmNQk0+zMGuxom9E8bRMZC973JWhNtCVwbqJfA0t2U0Z+PVk7TeOjnate1HSeSy\nCaMdJ0PSfpj3vhgbkvrxd4WpJ6tOHXfuaxby2E6pi3CIg8kG2+Lmpttpa/jhUqXtepZCknbWpaEL\nPMbTjPXT653ySEr4qEJd6GfXtp5x/kSGm83S9mXbAHo6grLZBFDTyrBnAht6NlidnZuF+3JRKL3i\n17kWAm5nWOrOxwyf+9pOeVwOv8gruJPJBps2/r8uMJuu3rKoV1Maae3NWZOFPFn1xbTkUbc2/S8a\nGE77PrG5brJ0Xy4Kpbfxp3UJC6ejiqntGic7K3lU9kvVubx52p3jgt6lOZjDJTCbzfc25LkbNmuy\nkCervpiWPOo2rv+5BoYD9HWTpftyUSi94s9qumW7u9QUhMxWnrg0APVirOpc3jxdxxIFvTNhkDmp\ne2UWJjFd8Debxe+4dkxKFm6CRTFF5OHyaCqDKv2oDC7pZum+XBRKb+rJahppPJvVT8tm6qyb/gUx\nv12m3zr7Zzs7lk4GrTukxi0xbsErrt6yaOe4NNLuhs3ajJLWzbVI5qusXXaT7Pi2da22zsthd3/R\nKP2IP8sgT6qY2uEY9HmbG8qErt4Xr12cy/Q3i3bO0uRQhnYsmvkqS9oRmC+rvIpI6Uf8SQN06dIx\nefXYTJ11AZ+C60WZfqfFVO9D5ww5t0dcvSVtZ1NwsjQeYGVox6zejSKStmw2z8cF8StzPYofvwNp\n4rYH9xTB97eI5FEvac4uiEPasbvJs+9kSUfi8VcN27jtpnu6cdqYBe3y/MjKHCPt2N2UwZSXhtKb\netqJzfQw7p5unn6nIY96ydMcI+3Y3ZTBlJcGMfUkpKzBmaqEmGPaSzjkBPUSFq9d3BIPv0wk7Tvt\n1gti6mkjZQ7OVCXEHNM+goNkAldenmKMXT+GrZdt7bBkyUjSd8qkF0TxJ6Db7X/dQlljpZcR3UEy\nNgfMFJEkfadMekFs/AnodvtfN1HWDTZlQxcoLa8zBtqBa98pk16QEX8CsjzNShC6AepVx0HQXe9G\nyqQXRPEnQGzHgtCK7iAZ4wEzXUaZ9EJqUw8R9QIYA/BzZj6XiE4HcBuAOQAeAHAxMx9Om4+OTnjX\niCufILQSeO90i1dPEsqkF1K7cxLRRwCMADjBV/y3Axhl5tuI6AYAO5n5elMaSd05y7K7ThAEIQ86\n4s5JRPMALAdwo/83AXgrgDv8WzYDWJkmDxNlWkUXBEEoCmlNPRsBfBTA8f7fgwCeY+Yj/t/7AJyq\nepCI1gJYCwBDQ0OJMi/TKrogCLLxsSgkHvET0bkAnmLmsKOuaglfaUti5k3MPMLMI3Pnzk0kQ5lW\n0QWh6pRpg1O3k8bUcw6AdxDRbniLuW+FNwM4kYiCmcQ8AI+nktBAmVbRBaHqiGm2OCRW/Mx8JTPP\nY+YFAC4E8A1mXg3gmwDe6d+2BsDdqaXUIDszBaE8iGm2OOSxc/djAG4jos8A+AGAm3LIo4nszBSE\nclCkoyCrTiYbuJj5W8x8rv/5UWZ+HTO/kpnfxcwvZZGHIAjlRkyzxUFi9QiC0BbKtMGp2xHFLwhC\n2xDTbDGQWD2CIAgVQxS/IAhCxRDFLwiCUDFE8QuCIFQMUfyCIAgVo2u9esoSDKoscgqC0D10peKP\nxukPgkEBKJRSLYucgiB0F11p6ilLMKiyyCkIQnfRlYq/LMGgyiKnIAjdRVcq/rLE6S+LnIIgdBdd\nqfjLEgyqLHIKgtBddOXiblmCQZVFTkEQugtiVp6M2FZGRkZ4bGys02IIgiCUCiLawcwjrs91palH\nEARB0COKXxAEoWJ0pY1fEITiIbvUi4MofkEQckd2qRcLMfUIgpA7sku9WIjiFwQhd2SXerEQxS8I\nQu7ILvViIYpfEITckV3qxSKx4iei04jom0T0MBE9RESX+9fnENHXiGiX//9J2YkrCEIZGV49jBWb\nVmBg/gBAwMD8AazYtEIWdjtE4p27RHQKgFOY+QEiOh7ADgArAbwPwAFm/iwRXQHgJGb+mCkt2bkr\nCILgTtt37jLzE8z8gP/5BQAPAzgVwHkANvu3bYb3YyAIgiAUhExs/ES0AMDZAO4H8HJmfgLwfhwA\nvEzzzFoiGiOisaeffjoLMQRBEAQLUit+IpoN4O8BrGPm522fY+ZNzDzCzCNz585NK4YgCIJgSSrF\nT0Q1eEp/CzOP+pef9O3/wTrAU+lEFARBELIkjVcPAbgJwMPM/PnQV18BsMb/vAbA3cnFEwRBELIm\nTayecwBcDGCciH7oX/s4gM8CuJ2IPgBgL4B3pRNREARByJLEip+ZvwuANF/LrgxBEISCIjt3BUEQ\nKoYofkEQhIohil8QBKFiiOIXBEGoGKL4BUEQKoYofkEQhIohil8QBKFiiOIXBEGoGKL4BUEQKoYo\nfkEQhIohil8QBKFiiOIXBEGoGKL4BUEQKoYofkEQhIohil8QBKFiiOIXBEGoGKL4BUEQKoYofkEQ\nhIohil8QBKFiiOIXBEGoGKL4BUEQKoYofkEQhIohil8QBKFiiOIXBEGoGH15JUxEbwdwLYBeADcy\n82fzymt8yzi2X7UdE3snUJ9TBwA0DjQwMDSAJVcvwfDqYac0BoYGsHDZQuy6d1fz73A6zXv3TIB6\nCTzFGJhvn1deRMsQlSfue9f0rGTZMwEQAPau1wfrWHrt0szz1bVJtB1N7Zo1Kpl0/UVXZtvrruVS\nPf/Q7Q+hsb8BwK6dnOogJBcAbLt8mzIv27ZP0zdjZbVor7JDzJx9okS9AH4K4HcA7APwrwDew8w/\nVt0/MjLCY2NjifIa3zKOe9beg8lDk8rva7NqWLFpRexLYEojnA4A7b02eeWFqgxheeK+d03PVZYw\nvf29OO/m8zLL16b9dOTVZmn7VG1WDYvWLMLOzTutruvStq1jFaZ2skGVT0+tBzzN4KlWvdPb34uz\nP3C2srzRcqTpmy6yRunk+62DiHYw84jrc3mZel4H4BFmfpSZDwO4DcB5eWS0/artxsaaPDSJ7Vdt\nT5VGOB3TvTZ55YVKrrA8cd+7pucqS5ipw1OZ5mvTfjryarO0fWry0CR2bNphfV2XdlLZAHM72aDK\nZ3pyeobSD/LSlTcqQ5q+6SJrlE6+31mTl6nnVAA/C/29D8DrwzcQ0VoAawFgaGgocUYTeydS32OT\nRlZ55YUu3+B63Pdpr2d1T5J809Z5Hm2WRZ9SKUjTddu0Xcqbpm5cn9WVK5pOFuVK+myn3u+syWvE\nT4prLa3KzJuYeYSZR+bOnZs4o4GhgdT32KQR3JdVWlmjyze4Hvd92utZ3ZMk37R1nkebZdGnqFf1\nGumv28rgUt40deP6rK5c0XSyKFfSZzv1fmdNXop/H4DTQn/PA/B4HhktuXoJarNq2u9rs2rNBaWk\naYTTMd1rk1deqOQKyxP3vWt6rrKE6e3vzTRfm/bTkVebpe1TtVk1LF672Pq6Lu2ksgHmdrJBlU9P\nrUep4Hv7e7XljcqQpm+6yBqlk+931uSl+P8VwEIiOp2I+gFcCOAreWQ0vHoYKzatwMD8AYA8D4H6\nYB0gYGD+gNViTDSNgfkDGLl0pOXvIJ2We3F0lGKbV16oyhCWJ+571/SsZQFa5n/1wbpxwTBJvqY2\nibajrl2zRieTqr/oyrz8uuXW113Kpevv9cF68564dnKuAz+flbesxPmbz1fmpStvVIY0fdNKVpjb\nqxvIxasHAIhoGYCN8Nw5b2bmq3X3pvHqEQRBqCpJvXpy8+Nn5nsB3JtX+oIgCEIyZOeuIAhCxRDF\nLwiCUDFE8QuCIFQMUfyCIAgVIzevHichiJ4GsCfh4ycDeCZDcbJG5EtOkWUDRL60FFm+IssGHJVv\nPjM774AthOJPAxGNJXFnahciX3KKLBsg8qWlyPIVWTYgvXxi6hEEQagYovgFQRAqRjco/k2dFiAG\nkS85RZYNEPnSUmT5iiwbkFK+0tv4BUEQBDe6YcQvCIIgOCCKXxAEoWKUWvET0duJ6CdE9AgRXdEh\nGW4moqeI6MHQtTlE9DUi2uX/f5J/nYjoL3x5f0REr81ZttOI6JtE9DARPURElxdMvmOJ6PtEtNOX\nb4N//XQiut+X78t+aG8Q0TH+34/43y/IUz4/z14i+gERfbWAsu0monEi+iERjfnXCtG2fp4nEtEd\nRPRvfh98Y1HkI6JX+fUW/HueiNYVSL4P++/Eg0T0Jf9dya7vMXMp/8EL9/z/AJwBoB/ATgBndkCO\nNwN4LYAHQ9euAXCF//kKAH/qf14GYBu8CPVvAHB/zrKdAuC1/ufjAfwUwJkFko8AzPY/1wDc7+d7\nO4AL/es3ALjU/3wZgBv8zxcC+HIb2vcjAP4WwFf9v4sk224AJ0euFaJt/Tw3A/h9/3M/gBOLJF9I\nzl4AvwAwvwjywTu69jEA9VCfe1+Wfa8tFZtT5bwRwD+G/r4SwJUdkmUBWhX/TwCc4n8+BcBP/M9/\nDeA9qvvaJOfdAH6niPIBmAXgAXhnMz8DoC/azgD+EcAb/c99/n2Uo0zzAGwH8FYAX/Vf+kLI5uez\nGzMVfyHaFsAJvvKiIsoXkel3AdxXFPlw9MzyOX5f+iqA38uy75XZ1KM60P3UDskS5eXM/AQA+P+/\nzL/eMZn96d/Z8EbVhZHPN6X8EMBTAL4Gbxb3HDMfUcjQlM//fgLAYI7ibQTwUQDT/t+DBZIN8M6x\n/ici2kFEa/1rRWnbMwA8DeAW31R2IxEdVyD5wlwI4Ev+547Lx8w/B/A5AHsBPAGvL+1Ahn2vzIo/\n9kD3AtIRmYloNoC/B7COmZ833aq4lqt8zDzFzK+BN7p+HYBfM8jQNvmI6FwATzHzjvBlQ/6daNtz\nmPm1AJYC+BARvdlwb7vl64NnAr2emc8GcBCe6URHp96NfgDvAPB3cbcqruXV904CcB6A0wG8AsBx\n8NpYl7+zbGVW/G070D0BTxLRKQDg//+Uf73tMhNRDZ7S38LMo0WTL4CZnwPwLXj20xOJKDgdLixD\nUz7/+wEAB3IS6RwA7xLcw7sAAAGzSURBVCCi3QBug2fu2VgQ2QAAzPy4//9TAO6E98NZlLbdB2Af\nM9/v/30HvB+CosgXsBTAA8z8pP93EeR7G4DHmPlpZp4EMArgN5Fh3yuz4m/bge4J+AqANf7nNfBs\n68H19/oeAm8AMBFMK/OAiAjATQAeZubPF1C+uUR0ov+5Dq/DPwzgmwDeqZEvkPudAL7BvmEza5j5\nSmaex8wL4PWtbzDz6iLIBgBEdBwRHR98hmenfhAFaVtm/gWAnxHRq/xLSwD8uCjyhXgPjpp5Ajk6\nLd9eAG8goln+OxzUXXZ9rx2LJ3n9g7fS/lN4duGrOiTDl+DZ4Sbh/fJ+AJ59bTuAXf7/c/x7CcBf\n+fKOAxjJWbY3wZvy/QjAD/1/ywok368D+IEv34MAPulfPwPA9wE8Am8Kfox//Vj/70f8789oUxu/\nBUe9egohmy/HTv/fQ0H/L0rb+nm+BsCY3753ATipYPLNArAfwEDoWiHkA7ABwL/578WtAI7Jsu9J\nyAZBEISKUWZTjyAIgpAAUfyCIAgVQxS/IAhCxRDFLwiCUDFE8QuCIFQMUfyCIAgVQxS/IAhCxfj/\nGzxckt2MqX8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad2fffb350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data[\"BloodPressure\"].values,color='purple')\n",
    "plt.title(\"Distribution of BloodPressure\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EzihtOfCb/GqVT+65286t8+W3vdBJsTwaGQnRtrJfKpfAM1wn+/GngggYSdu9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\nYcjALwiC0GHIwC8IgtBhyMAvCILQYfx/kC4Gtgwi0Y4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad301c3e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data[\"SkinThickness\"].values,color='purple')\n",
    "plt.title(\"Distribution of SkinThickness\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yarc8X77p5bHb1928LvVYnzkxazeuba6nBvbt2sfs7tnE8o/+NnnjZHobsaS1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\nilIzVPEriqLUDFX8iqIoNeP/A1TAMCz+SE8KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad301c3d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data[\"Insulin\"].values,color='purple')\n",
    "plt.title(\"Distribution of Insulin\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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47oCx5K/PeXTYZFUhsyrss7S0vliby3Oo+tbDdz5cpwuuuvsqAPEJYOF+rLJ6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\n5OnYygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad39ddda10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data[\"BMI\"].values,color='purple')\n",
    "plt.title(\"Distribution of BMI\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6a0mJTTJSTPu2rhscW2yk3friAG2u7NMGoYoziloLoHBcwTsbJ4ucduODy34B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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad39d5f910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data[\"DiabetesPedigreeFunction\"].values,color='purple')\n",
    "plt.title(\"Distribution of DiabetesPedigreeFunction\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nhj3svG5nzx/NeAONZASnrq68HhplenvYltIuZVYR/elqZxP6z6X9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad39dbdd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data[\"Age\"].values,color='purple')\n",
    "plt.title(\"Distribution of Age\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 8)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = data['Outcome'].values\n",
    "X = data.drop('Outcome', axis = 1)\n",
    "#用于后续显示权重系数对应的特征\n",
    "columns = X.columns\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#缺失值填补\n",
    "from sklearn.preprocessing import Imputer\n",
    "feature_list_to_fill_NA = ['BloodPressure','SkinThickness','Insulin','BMI']\n",
    "imp = Imputer(missing_values = 0,copy=False,axis = 0,strategy = 'mean')\n",
    "X[feature_list_to_fill_NA] = imp.fit_transform(X[feature_list_to_fill_NA])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### default Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [0.46129644 0.46510588 0.56426612 0.44377717 0.46617809]\n",
      "cv logloss is: 0.48012474004701106\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr= LogisticRegression()\n",
    "# 交叉验证用于评估模型性能和进行参数调优（模型选择）\n",
    "#分类任务中交叉验证缺省是采用StratifiedKFold\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "loss = cross_val_score(lr, X_train, y_train, cv=5, scoring='neg_log_loss')\n",
    "print 'logloss of each fold is: ',-loss\n",
    "print'cv logloss is:', -loss.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 正则化的 Logistic Regression及参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#需要调优的参数\n",
    "# 请尝试将L1正则和L2正则分开，并配合合适的优化求解算法（slover）\n",
    "#tuned_parameters = {'penalty':['l1','l2'],\n",
    "#                   'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "#                   }\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00075865, 0.00092106, 0.00095954, 0.00140195, 0.00123024,\n",
       "        0.0015892 , 0.00140076, 0.00121465, 0.00113144, 0.00109921,\n",
       "        0.00099959, 0.00117793, 0.00097857, 0.00145788]),\n",
       " 'mean_score_time': array([0.00136919, 0.00082717, 0.00087485, 0.00103264, 0.00078616,\n",
       "        0.00123506, 0.00105343, 0.00073638, 0.00073285, 0.00082641,\n",
       "        0.00101023, 0.0009232 , 0.00070562, 0.00089679]),\n",
       " 'mean_test_score': array([-0.69314718, -0.64214833, -0.6721329 , -0.52844007, -0.48658814,\n",
       "        -0.47999943, -0.48043349, -0.48017599, -0.48089987, -0.48086932,\n",
       "        -0.48095022, -0.48095123, -0.48095644, -0.48095956]),\n",
       " 'mean_train_score': array([-0.69314718, -0.6412946 , -0.67079105, -0.52380684, -0.47502508,\n",
       "        -0.46674403, -0.4622879 , -0.46214818, -0.46206768, -0.46206619,\n",
       "        -0.46206531, -0.4620653 , -0.46206529, -0.46206529]),\n",
       " 'param_C': masked_array(data=[0.001, 0.001, 0.01, 0.01, 0.1, 0.1, 1, 1, 10, 10, 100,\n",
       "                    100, 1000, 1000],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_penalty': masked_array(data=['l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1',\n",
       "                    'l2', 'l1', 'l2', 'l1', 'l2'],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 0.001, 'penalty': 'l1'},\n",
       "  {'C': 0.001, 'penalty': 'l2'},\n",
       "  {'C': 0.01, 'penalty': 'l1'},\n",
       "  {'C': 0.01, 'penalty': 'l2'},\n",
       "  {'C': 0.1, 'penalty': 'l1'},\n",
       "  {'C': 0.1, 'penalty': 'l2'},\n",
       "  {'C': 1, 'penalty': 'l1'},\n",
       "  {'C': 1, 'penalty': 'l2'},\n",
       "  {'C': 10, 'penalty': 'l1'},\n",
       "  {'C': 10, 'penalty': 'l2'},\n",
       "  {'C': 100, 'penalty': 'l1'},\n",
       "  {'C': 100, 'penalty': 'l2'},\n",
       "  {'C': 1000, 'penalty': 'l1'},\n",
       "  {'C': 1000, 'penalty': 'l2'}],\n",
       " 'rank_test_score': array([14, 12, 13, 11, 10,  1,  3,  2,  5,  4,  6,  7,  8,  9],\n",
       "       dtype=int32),\n",
       " 'split0_test_score': array([-0.69314718, -0.64371675, -0.66946739, -0.52816851, -0.48618115,\n",
       "        -0.4678555 , -0.46345702, -0.46129644, -0.4610858 , -0.46088502,\n",
       "        -0.46086727, -0.4608487 , -0.46084556, -0.46084513]),\n",
       " 'split0_train_score': array([-0.69314718, -0.64085132, -0.66189542, -0.52529099, -0.47823727,\n",
       "        -0.47085398, -0.46684286, -0.46669378, -0.46662378, -0.46662226,\n",
       "        -0.46662151, -0.46662149, -0.46662149, -0.46662148]),\n",
       " 'split1_test_score': array([-0.69314718, -0.64113725, -0.67517494, -0.52518603, -0.47166521,\n",
       "        -0.47077815, -0.46426402, -0.46510588, -0.46463413, -0.46473286,\n",
       "        -0.46468513, -0.46469927, -0.46469237, -0.46469595]),\n",
       " 'split1_train_score': array([-0.69314718, -0.64188719, -0.67797602, -0.52532854, -0.47970363,\n",
       "        -0.47076994, -0.46692671, -0.46678157, -0.4667161 , -0.4667146 ,\n",
       "        -0.4667139 , -0.46671388, -0.46671388, -0.46671388]),\n",
       " 'split2_test_score': array([-0.69314718, -0.64575466, -0.6680453 , -0.5512625 , -0.54752795,\n",
       "        -0.54301469, -0.56469268, -0.56426612, -0.56847756, -0.56834734,\n",
       "        -0.56879323, -0.56879096, -0.56882978, -0.5688357 ]),\n",
       " 'split2_train_score': array([-0.69314718, -0.63908575, -0.66162164, -0.5135644 , -0.45564351,\n",
       "        -0.44783203, -0.44194631, -0.44184498, -0.44173179, -0.44173047,\n",
       "        -0.44172922, -0.4417292 , -0.44172919, -0.44172919]),\n",
       " 'split3_test_score': array([-0.69314718, -0.63856226, -0.67581323, -0.50975335, -0.45503472,\n",
       "        -0.44706595, -0.44365797, -0.44377717, -0.44397624, -0.44400066,\n",
       "        -0.44403048, -0.4440332 , -0.44403529, -0.44403656]),\n",
       " 'split3_train_score': array([-0.69314718, -0.64333601, -0.67874616, -0.5309552 , -0.48382629,\n",
       "        -0.47509784, -0.47066685, -0.47052445, -0.47044461, -0.47044314,\n",
       "        -0.47044228, -0.47044226, -0.47044225, -0.47044225]),\n",
       " 'split4_test_score': array([-0.69314718, -0.64152376, -0.67221592, -0.52767402, -0.47216115,\n",
       "        -0.47104103, -0.46581595, -0.46617809, -0.46606591, -0.46612355,\n",
       "        -0.46611753, -0.4661268 , -0.46612193, -0.46612722]),\n",
       " 'split4_train_score': array([-0.69314718, -0.64131272, -0.67371601, -0.52389506, -0.47771467,\n",
       "        -0.46916638, -0.46505676, -0.46489609, -0.46482211, -0.46482048,\n",
       "        -0.46481965, -0.46481964, -0.46481963, -0.46481963]),\n",
       " 'std_fit_time': array([0.000228  , 0.0002078 , 0.00050361, 0.00066833, 0.00066164,\n",
       "        0.00116109, 0.00050195, 0.00039738, 0.00040781, 0.0001284 ,\n",
       "        0.00011005, 0.00035083, 0.0001106 , 0.00048484]),\n",
       " 'std_score_time': array([5.68570366e-04, 2.16264109e-04, 3.27076180e-04, 3.07135119e-04,\n",
       "        1.05735627e-04, 4.83281891e-04, 5.83219635e-04, 7.60307242e-05,\n",
       "        7.24019404e-05, 2.05156349e-04, 4.39874626e-04, 3.01738108e-04,\n",
       "        4.34541260e-05, 2.54455767e-04]),\n",
       " 'std_test_score': array([0.        , 0.00243715, 0.00305426, 0.0132657 , 0.03205884,\n",
       "        0.03276814, 0.04294177, 0.04285084, 0.04453508, 0.04448689,\n",
       "        0.04466313, 0.04466183, 0.04467789, 0.04467945]),\n",
       " 'std_train_score': array([0.        , 0.00138524, 0.00757197, 0.00566626, 0.0099252 ,\n",
       "        0.00965834, 0.01033363, 0.01031565, 0.01033125, 0.01033118,\n",
       "        0.01033136, 0.01033136, 0.01033136, 0.01033136])}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the complete results (list of named tuples)\n",
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4799994349368312\n",
      "{'penalty': 'l2', 'C': 0.1}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IJSosh/M6Nrc6IqWUD3jay+t/gAP4j3v9alzNTVm4hrG/zOuRqXLlJyeTNX8+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\nKEoppbzi/wGuZNN+NacnhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad39edf050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    pyplot.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    pyplot.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'neg-logloss' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lyc/anaconda2/lib/python2.7/site-packages/sklearn/model_selection/_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_part, X_val, y_train_part, y_val = train_test_split(X_train, y_train, train_size = 0.8,random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##  default SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for classifier LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.79      0.81        89\n",
      "          1       0.53      0.62      0.57        34\n",
      "\n",
      "avg / total       0.76      0.74      0.75       123\n",
      "\n",
      "\n",
      "Confusion matrix:\n",
      "[[70 19]\n",
      " [13 21]]\n"
     ]
    }
   ],
   "source": [
    "#1.4.1.2. 得分与概率\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn import metrics\n",
    "SVC1 = LinearSVC().fit(X_train, y_train)\n",
    "#在校验集上测试，估计模型性能\n",
    "y_predict = SVC1.predict(X_val)\n",
    "\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (SVC1, metrics.classification_report(y_val, y_predict)))\n",
    "print(\"Confusion matrix:\\n%s\" % metrics.confusion_matrix(y_val, y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 线性SVM正则参数调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "线性SVM LinearSVC的需要调整正则超参数包括C（正则系数，一般在log域（取log后的值）均匀设置候选参数）和正则函数penalty（L2/L1） \n",
    "\n",
    "采用交叉验证，网格搜索步骤与Logistic回归正则参数处理类似，在此略。\n",
    "\n",
    "这里我们用校验集（X_val、y_val）来估计模型性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fit_grid_point_Linear(C, X_train, y_train, X_val, y_val):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC2 =  LinearSVC( C = C)\n",
    "    SVC2 = SVC2.fit(X_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC2.score(X_val, y_val)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.756097560976\n",
      "accuracy: 0.739837398374\n",
      "accuracy: 0.739837398374\n",
      "accuracy: 0.747967479675\n",
      "accuracy: 0.747967479675\n",
      "accuracy: 0.764227642276\n",
      "accuracy: 0.715447154472\n"
     ]
    },
    {
     "data": {
      "image/png": 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n9VNNFma2v5nNMbP5ZnZmA/dfbmYzspe5ZrYke/sQM3vSzGaZ2fNmdmiacYqI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\nEBGRvJQsREQkLyULERHJ6/8BRHTYHKEBavYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad39d5fb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-3, 3, 7)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份  \n",
    "#penalty_s = ['l1','l2']\n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "#    for j, penalty in enumerate(penalty_s):\n",
    "    tmp = fit_grid_point_Linear(oneC, X_train, y_train, X_val, y_val)\n",
    "    accuracy_s.append(tmp)\n",
    "\n",
    "x_axis = np.log10(C_s)\n",
    "#for j, penalty in enumerate(penalty_s):\n",
    "pyplot.plot(x_axis, np.array(accuracy_s), 'b-')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuracy' )\n",
    "\n",
    "pyplot.show()\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RBF核SVM正则参数调优\n",
    "\n",
    "RBF核是SVM最常用的核函数。\n",
    "RBF核SVM 的需要调整正则超参数包括C（正则系数，一般在log域（取log后的值）均匀设置候选参数）和核函数的宽度gamma\n",
    "C越小，决策边界越平滑； \n",
    "gamma越小，决策边界越平滑。\n",
    "\n",
    "采用交叉验证，网格搜索步骤与Logistic回归正则参数处理类似，在此略。\n",
    "\n",
    "这里我们用校验集（X_val、y_val）来估计模型性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fit_grid_point_RBF(C, gamma, X_train, y_train, X_val, y_val):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC3 =  SVC( C = C, kernel='rbf', gamma = gamma)\n",
    "    SVC3 = SVC3.fit(X_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC3.score(X_val, y_val)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.723577235772\n",
      "accuracy: 0.723577235772\n",
      "accuracy: 0.723577235772\n",
      "accuracy: 0.723577235772\n",
      "accuracy: 0.723577235772\n",
      "accuracy: 0.723577235772\n",
      "accuracy: 0.821138211382\n",
      "accuracy: 0.723577235772\n",
      "accuracy: 0.723577235772\n",
      "accuracy: 0.723577235772\n",
      "accuracy: 0.756097560976\n",
      "accuracy: 0.821138211382\n",
      "accuracy: 0.959349593496\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 0.772357723577\n",
      "accuracy: 0.861788617886\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 0.829268292683\n",
      "accuracy: 0.943089430894\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n",
      "accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-2, 2, 5)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份 \n",
    "gamma_s = np.logspace(-2, 2, 5)  \n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, X_train, y_train, X_val, y_val)\n",
    "        accuracy_s.append(tmp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TQwkhnI/l9GmS7rqb/L17afvaApoNkH/nNQTVjoayjmr6i9Z6BpALTLTnhbXW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\nxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad302b5ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_s1 =np.array(accuracy_s).reshape(len(C_s),len(gamma_s))\n",
    "x_axis = np.log10(C_s)\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    pyplot.plot(x_axis, np.array(accuracy_s1[:,j]), label = ' Test - log(gamma)' + str(np.log10(gamma)))\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuracy' )\n",
    "\n",
    "pyplot.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
